Smart energy distribution methods and systems for electric vehicle charging

ABSTRACT

A power management system smartly allocates the available power at a location to support more electric vehicles than would otherwise be possible. Power managers can intelligently allocate that power based on the real-time needs of vehicles. A smart energy distribution system can estimate each vehicle&#39;s current charge level and use such information to efficiently provide electric vehicle charging. The system can respond dynamically to vehicle charge levels, current readings, and/or electrical mains readings, allocating more current where it is needed. The charger profiles can include historic charge cycle information, which can be analyzed under a set of heuristics to predict future charging needs. A local electric vehicle charging mesh network can be provided, which transmits data packets among short-range transceivers of multiple power managers. The charging mesh network is connected to a remote server. The power managers and the charging mesh network can intelligently allocate power to multiple electric vehicles.

RELATED APPLICATION DATA

This application is a continuation of U.S. patent application Ser. No.14/663,398, filed on Mar. 19, 2015, which claims the benefit of U.S.provisional patent application Ser. No. 61/968,311, filed Mar. 20, 2014,and claims the benefit of U.S. provisional patent application Ser. No.61/979,186, filed Apr. 14, 2014, which are hereby incorporated byreference.

FIELD

This disclosure relates to electric vehicles, and, more particularly, tosmart energy distribution methods and systems for electric vehiclecharging allocation techniques and multi-level garage electrical vehiclecharging infrastructure.

BACKGROUND

The adoption of electric vehicles, plug-in hybrid electric vehicles, andthe like, continues at a rapid pace. The charging infrastructure isstill in its infancy and many challenges remain including scaling,efficiency, and cost barriers. Conventional charging and energydistribution systems lack any significant level of built-inintelligence, and as a result, the methods used for charging electricvehicles are usually wasteful and inefficient.

Moreover, as the deployment of electric vehicles increases, the charginginfrastructure must be adapted to meet demand. Multi-level parkingspaces used in apartment complexes, shopping malls, downtown parkinggarages, and the like, suffer from a variety of unique problems such asthe coordination of charging devices among the various levels. Many suchparking spaces are constructed of dense materials such as cement andsteel, which impede conventional wireless networking solutions. This inturn diminishes the coordination and communication of differentcomponents of a charging system or network, and consequently, theintelligence of such conventional systems are either difficult toimplement, too costly to install, or simply impossible.

Accordingly, a need remains for improved methods and systems forefficiently and intelligently distributing energy to electric vehicles.Embodiments of the invention address these and other limitations in theprior art.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a smart energy distribution system for providingsmart energy distribution via an intelligent control signal inaccordance with various embodiments of the present invention.

FIG. 2 illustrates a power balancing system from a limited power supplyin accordance with embodiments of the present invention.

FIG. 3 illustrates a block diagram of a power manager and associatedpower regulation components in accordance with embodiments of thepresent invention.

FIG. 4 shows a flow diagram illustrating a technique for providing smartenergy distribution via an intelligent control signal in accordance withembodiments of the present invention.

FIG. 5 illustrates a chart and associated technique for smart energydistribution to various electric vehicles in accordance with embodimentsof the present invention.

FIG. 6 shows a flow diagram illustrating another technique for providingsmart energy distribution via an intelligent control signal inaccordance with embodiments of the present invention.

FIG. 7 illustrates a graph demonstrating the regulation of the electricvehicle charging in accordance with embodiments of the presentinvention.

FIG. 8 illustrates a diagram of a multi-circuit multi-parking-levelelectric vehicle system including a local electric vehicle charging meshnetwork in accordance with embodiments of the present invention.

FIG. 9 illustrates a diagram of a multi-circuit multi-parking-levelapartment complex including distributed power managers for controllingthe distribution of power to electric vehicles and to other apartmentappliances in accordance with embodiments of the present invention.

The foregoing and other features of the invention will become morereadily apparent from the following detailed description, which proceedswith reference to the accompanying drawings.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to embodiments of the inventiveconcept, examples of which are illustrated in the accompanying drawings.The accompanying drawings are not necessarily drawn to scale. In thefollowing detailed description, numerous specific details are set forthto enable a thorough understanding of the inventive concept. It shouldbe understood, however, that persons having ordinary skill in the artmay practice the inventive concept without these specific details. Inother instances, well-known methods, procedures, components, circuits,and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. For example, a first electric vehicle could betermed a second electric vehicle, and, similarly, a second electricvehicle could be termed a first electric vehicle, without departing fromthe scope of the inventive concept.

Like numbers refer to like elements throughout. The terminology used inthe description of the inventive concept herein is for the purpose ofdescribing particular embodiments only and is not intended to belimiting of the inventive concept. As used in the description of theinventive concept and the appended claims, the singular forms “a,” “an,”and “the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. It will also be understood that theterm “and/or” as used herein refers to and encompasses any and allpossible combinations of one or more of the associated listed items. Itwill be further understood that the terms “comprises” and/or“comprising,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

Reference is often made herein to “electric vehicles.” It will beunderstood that such vehicles can include plug-in hybrid vehicles, pureelectric vehicles, or any one of a variety of vehicles that operate ormove using at least some electricity. The term “control signal” asreferred to herein can be a “pilot signal,” or other suitable controlsignal. The term “pilot signal” as referred to herein can be a lowvoltage connection that is used to control a level of current draw thatthe electric vehicle requests or is allowed to request.

Embodiments of the invention include a power management system thatsmartly allocates the available power at a location to support moreelectric vehicles than would otherwise be possible. When a power managerhas the information about the amount of available power available on agiven supply, it can intelligently allocate that power based on thereal-time needs of vehicles. By monitoring the current draw on eachelectric vehicle using a current sensor or by accessing the electricvehicle's state of charge through an API accessed through a remoteaccess or server network (which can include information about at leastone of user history or input, real-time data, or historical data), asmart energy distribution system disclosed herein can estimate eachvehicle's current charge level and use this information to provide theminimum amount of needed current with or without a buffer to theelectric vehicle. This system works with one or many electric vehiclesusing the charging system.

Such approach allows a site electrical capacity to be allocatedefficiently and uses a low voltage signal to have the electric vehiclesregulate charge levels internally, as further explained below. Thesystem can respond dynamically to at least one of vehicle charge levels,current readings, or electrical mains readings, allocating more currentwhere it is needed. At least one of cycle point or charger profiles forindividual electric vehicles can be determined and stored. The chargerprofiles can include historic charge cycle information, which can beused and analyzed under a set of heuristics to predict future chargingneeds, including expected time-of-day and charge level approximations.

A local electric vehicle charging mesh network can be provided, whichtransmits data packets among short-range transceivers of multiple powermanagers that are configured to be part of the local electric vehiclecharging mesh network. The local electric vehicle charging mesh networkcan be connected to a remote server via a cellular connection, which isdisposed at a location associated with a parking structure that providesa sufficient or reliable cellular reception. The power managers and thelocal electric vehicle charging mesh network can intelligently andunevenly allocate power to multiple electric vehicles using the localelectric vehicle charging mesh network. The remote server can provideanalytical information about the local electric vehicle charging meshnetwork, the power managers, the electric vehicles, and the like, asfurther described in detail below. The remote server can be located in ageographical location entirely different from the parking structure,such as at a control center in another city, state, or country.Alternatively, the remote server can be located proximate to or withinthe parking structure. In this embodiment, the remote server is “remote”to the mesh network.

FIG. 1 illustrates a smart energy distribution system 100 for providingsmart energy distribution via an intelligent control signal (e.g., 118)in accordance with various embodiments of the present invention. Thesmart energy distribution system 100 can include at least one ofelectric vehicles 105, current sensors 130, pilot signals 118, insulatedor isolated connectors 110, a remote server 145, a database of useridentifiers (IDs) 155, control logic 160, power managers (e.g., 115, 120and 125), or a power source 140. Alternatively or in addition, thecontrol logic 160 can be localized to each power manager. For example,the power manager 115 can include control logic 135. One or more of thepower managers 115, 120, and 125 can be connected to the power source140. The one or more power managers 115, 120, and 125 can use a pilotsignal 118 to regulate the amount of power an electric vehicle 105 isallowed to draw. The pilot signals 118 supplied to the differentvehicles 105 can be varied based on the control logic 160, which canoptionally be localized (e.g., 135) or modified by information receivedover a network connection 150. The network connection 150 can include orotherwise be connected to, for example, the Internet. The informationreceived over the network connection 150 can include real-timeinformation about energy demand elsewhere at one or more other locations(not shown).

At least one of the power managers 115, 120, or 125 can each include orbe associated with at least one of a microprocessor (e.g., 165), a pilotsignal generator (e.g., 170), an electric vehicle connector cord (e.g.,175), or a wired or wireless communication connection (e.g., 180) tointerface with other power managers or with a network. At least one ofthe power managers 115, 120, or 125 can determine the state of charge(e.g., 185) of one or more of the electric vehicles 105 (e.g., theapproximate point in the electric vehicle's charge cycle). It will beunderstood that while three electric vehicles 105 are shown, anysuitable number of electric vehicles can be used with or otherwisecharged by the smart energy distribution system 100.

Using the approximate state of charge 185 of an electric vehicle 105 andthe pilot signal 118, charge between multiple power managers (e.g., 115,120, and 125) can be managed using the control logic (e.g., 160 and 135)to have the electric vehicles 105 internally regulate how much powereach will draw from the power source 140. In other words, the powermanagers (e.g., 115, 120, and 125) can generate and control the pilotsignal 118 for each electric vehicle, which can cause each electricvehicle 105 to at least one of limit or alter the amount of power thatit will draw over time.

The information about the electric vehicle's state of charge 185 (e.g.,point in its charge cycle) can be stored in the remote server 145 andaccessed via the network 150. The power managers (e.g., 115, 120, and125) can use pilot signals 118 to control the electric vehicles'internal control of current draw to regulate amperage so that the totalamperage for a given charging location does not exceed the amount ofpower available at the given charging location. The power managers(e.g., 115, 120, and 125) can allocate available power to the electricvehicles 105 using the reading from current sensors 130 associated witheach of the corresponding power managers (e.g., 115, 120, and 125) andelectric vehicles 105 to predict the appropriate allocation of power toeach electric vehicle 105. The system can include a user input device190, which can allow a user (e.g., driver of the electric vehicle 105)to make a request for charging in which the request causes thecorresponding power manager (e.g., 115, 120, and 125) to maximizemileage for the electric vehicle 105, minimize energy costs for theelectric vehicle 105, and increase charging speed for the electricvehicle 105.

The smart energy distribution system 100 can include a combination ofElectric Vehicle Service Equipment (EVSE) and energy management device(e.g., power managers 115, 120, and 125), which can include or otherwiseinterface with an intelligent monitoring apparatus (e.g., 175 and 110)that includes the connector (e.g., 110). The connector 110 can includeone or more safety components for isolating high power from the user ofthe electric vehicle 105. The power managers (e.g., 115, 120, and 125)can communicate with the electric vehicles 105 using a low voltage pilotsignal 118. The smart energy distribution system can include a connector110 having one or more safety components that can dynamically regulatepower management through communication with combination EVSE powermanager devices (e.g., 115, 120, and 125) directly, indirectly, or overa network. The method of communication between EVSE can include at leastone of wireless, locally, connected, wired, Ethernet, mesh network, fullon/off timed system, or any suitable IP connected device.

The power managers (e.g., 115, 120, and 125) can communicate with eachother and with the remote server 145 to prioritize one electric vehicle105 over another electric vehicle 105 for charging. The power managers(e.g., 115, 120, and 125) can communicate with each other or with theremote server 145 to prioritize one electric vehicle 105 over anotherelectric vehicle 105 for charging. The prioritization can be based on atleast one of user inputs via the user input device 190, state of chargeinformation 185, a maximum rated charge level 195 of an installation, ora current sensor reading from the current sensor 130. The power managers(e.g., 115, 120, and 125) can communicate with each other and with theremote server 145 to generate a prioritization and average distribution198. The power managers (e.g., 115, 120, and 125) can communicate witheach other or with the remote server 145 to generate a prioritizationand average distribution 198. The prioritization and averagedistribution 198 can be regulated by customer input and rankings. Theprioritization and average distribution 198 can be regulated bypredictive time slicing gathered from past time slicing. Theprioritization and average distribution 198 can be regulated by adaptivelearning logic, which can improve the predications and prioritizations.

The prioritization and average distribution 198 can be regulated by howmuch the user wants to pay. The user can input via the user input device190 how many miles desired from a given charging session and prioritizeby a request to have the vehicle fully charged. The prioritization andaverage distribution 198 can be regulated by the control logic (e.g.,160 and 135) to provide a feedback loop. The feedback loop can provide arate schedule for an electrical utility company 142. The electricalutility company 142 can recommend what rate schedule the charginglocation can use including suggestions for a cheapest rate.

FIG. 2 illustrates a power balancing system 200 from a limited powersupply or supplies 205 (hereinafter referred to as power supply 205) inaccordance with embodiments of the present invention. For a givencharging location (such as a multi-car garage, series of parking spaces,apartment building, or the like) associated with the power supply 205,there is a given amount of installed power (e.g., installed Kilowatts(KW)) that is available. Different percentages (e.g., 215, 220, and 225)of this available pool 205 can be allocated to multiple electricvehicles (e.g., 230, 235, and 240), respectively. According toembodiments of the invention disclosed herein, the percentages can bedynamically changed and controlled. For example, the power managers(e.g., 115, 120, and 125 of FIG. 1) and the remote server 145 (ofFIG. 1) can dynamically change and control the different percentages ofthe available pool of power 205 that are allocated to each of theelectric vehicles (e.g., 230, 235, and 240). For example, the powermanagers (e.g., 115, 120, and 125 of FIG. 1) or the remote server 145(of FIG. 1) can dynamically change and control the different percentagesof the available pool of power 205 that are allocated to each of theelectric vehicles (e.g., 230, 235, and 240). The different percentagesin the aggregate 210 are equal to 100% of the allocable power, which maytake into account a buffer as further described below. Each allocationpercentage can be the same as or different from another allocationpercentage from among the allocation percentages.

FIG. 3 illustrates a block diagram of system 300 including a powermanager 305 and associated power regulation components (e.g., 320 and380) in accordance with embodiments of the present invention. The powermanager 305 can receive information from a current sensor 380, andtogether with the control logic section 320, regulate the charging powerof an electric vehicle 360. Although the control logic section 320 isshown as separate from the power manager 305, it will be understood thatthe power manager 305 can include the control logic section 320. Theelectric vehicle 360 can be allowed to draw using a pilot signal 340. Inother words, the control logic section 320 can adjust a signal level ofthe pilot signal 340, thereby controlling the maximum charge level ofthe electric vehicle 360. The current sensor 380 can provide a feedbackloop for at least one of the power manager 305 or the control logicsection 320 for further adjusting of the pilot signal 340. The electricvehicle charge level of the electric vehicle 360 can therefore becontrolled.

FIG. 4 shows a flow diagram 400 illustrating a technique for providingsmart energy distribution via an intelligent control signal inaccordance with embodiments of the present invention. The technique canbegin at 415, where an available and installed maximum power or maximumAmperes (hereinafter referred to as “amps”) can be determined. Theavailable and installed maximum power or maximum amps can be associatedwith a particular location such as a parking garage, apartment complex,parking lot, or the like. The flow can proceed to 405 where adetermination can be made whether power is requested by a user (e.g., byan electric vehicle). If no power is requested, then there need not beany smart resource allocation, and therefore, the flow proceeds to nosmart resource allocation at 410. Otherwise, if power is requested, thenthe system can initially distribute power to the electric vehiclesequally at 420. The power may be distributed according to the maximumamount of installed power (e.g., available kilowatts (KW)) or maximumamps for the location, with or without a buffer, as further describedbelow. The power may be distributed to the electric vehicles based onthe vehicle's rated acceptable or maximum levels.

At 425, the current sensor (e.g., 380 of FIG. 3) can be periodicallychecked to determine whether the amount of power allocated exceeds thereal current that the vehicle is drawing. At 430, a determination can bemade whether the amount of power distributed to the vehicles should bereallocated. In other words, the amount of current a vehicle is drawingover a period of time can be used to predict the rest of a chargingcycle, as further explained below. If the amount of power distributed tothe electric vehicles is to be reallocated, the flow can proceed to 432,where the energy can be intelligently and unevenly allocated among theelectric vehicles, and then to 425 for additional checking of thecurrent sensor for excess allocation. Otherwise, the flow proceeds alongthe NO path to end the technique. It will be understood that the stepsand elements of FIG. 4 need not occur in the order shown, but rather,can occur in a different order or with intervening steps, or withoutsome of the steps.

FIG. 5 illustrates a chart 500 and associated technique for smart energydistribution to five vehicles in accordance with embodiments of thepresent invention. As can be seen in FIG. 5, five electric vehicles (V1,V2, V3, V4, and V5) are shown. It will be understood that the number offive electric vehicles is exemplary, and any suitable number of electricvehicles can be associated with the inventive techniques describedherein. In this example, it is assumed that the given amount ofinstalled amps for the charging location is 100 amps (e.g., a circuitrated at 100 amps). The technique involves a series of steps, denoted by505 through 540. The number of amps that are intelligently allocated arenumerically shown in the five columns associated with vehicles V1through V5. The power managers (e.g., 305 of FIG. 3) can allocate anamount of allocable power among the vehicles V1 through V5. Theallocable amount of power can be a total amount of installed power for agiven location minus a buffer. The power managers can intelligentlyallocate the allocable amount of power according to a set of heuristics.

At 505, the user request indicates whether a person is trying to chargetheir electric vehicle at the present moment. The request can be made byan RFID signal or other suitable wired or wireless method. In this case,four of the five vehicles (i.e., V1, V2, V3, and V4) have requestedpower. V5 is fully charged and therefore is not requesting power at thistime.

At 510, the defined charge level of each electric vehicle is shown. Eachelectric vehicle in this case takes a maximum of 50 amps to charge.Because V5 is already charged it requires 0 amps to charge. The 100 ampcircuit cannot charge four vehicles at once with 50 amps each becausethe circuit only has 100 total amps of charging capacity, and four times50 amps would be 200 amps, which exceeds the 100 total amps of capacity.Pertaining to 515, where time (t)=0, each charging system cansimultaneously supply 25 amps from the 100 amp circuit to the fourelectric vehicles without exceeding the 100 amp installed limit, withouttaking into account any kind of buffer.

As shown at 520, to comply with electric governmentally imposed codes,each vehicle can receive 20 amps, for a total of 80 amps. V5 didn'trequest charging, so 0 amps are allocated to it. The remaining 20 amps(i.e., 100 minus 80) are not allocated to conform with government code.The remaining 20 amps provide a buffer between the total installed ampsand the amount of amps that are intelligently allocated to the variouselectric vehicles.

At 525, time (t)=1, a current sensor can read that V2 and V3 arecharging at a high level. This information can be accessed through acharge level application specific interface (API). By way of example, V2and V3 can request priority charging, and therefore, can be charged at ahigher priority. The higher priority charging can mean receiving atleast one of a charge earlier in time or at a higher power level.

At 530, time (t)=2, vehicles V1 and V4 are nearing the end of the chargecycle, and so V1 and V4 are allocated five amps each. The vehicles V2and V3 are not at the end of the charge cycle, and therefore vehicles V2and V3 can be allocated 15 more amps each, up to a total of 35 ampseach.

At 535, time (t)=3, another current draw reading can be performed, andit can be determined from the current draw reading that V1 and V4 arenearly finished charging. V2 and V3 continue to be allocated 35 ampseach to ensure that electrical code requirements are still met whileallowing V1 and V4 to safely finish charging.

At 540, time (t)=4, yet another current drawing reading can beperformed, thereby determining that V1 and V4 are entirely finishedcharging. Therefore, V2 and V3 can be allocated an increased charginglevel of 40 amps each, still maintaining compliance with the electricalcode requirements. In some embodiments, V2 and V3 are allocated theirmaximum charging level assuming that the code requirements can still bemet.

The power managers (e.g., 305 of FIG. 3) can intelligently allocate anallocable amount of power among the electric vehicles for charging theelectric vehicles. The allocable amount of power can be a total amountof installed power for a given location minus a buffer. The powermanagers can intelligently and unevenly allocate the allocable amount ofpower among the electric vehicles according to a set of heuristics. Forexample, the power managers can allocate, during a first time period, anequal fraction of the allocable amount of power to each of the electricvehicles. The power managers can allocate, during a second time period,a first fraction of the allocable amount of power to a first subset ofthe electric vehicles. The power managers can allocate during the secondtime period, a second fraction different from the first fraction of theallocable amount of power to a second subset of the vehicles. The powermanagers can allocate, during a third time period, a third fractiondifferent from the first and second fractions of the allocable amount ofpower to the first subset of electric vehicles. The power managers canallocate, during a fourth time period, a fourth fraction different fromthe first, second, and third fractions of the allocable amount of powerto the second subset of the electric vehicles, and so forth.

FIG. 6 shows a flow diagram 600 illustrating another technique forproviding smart energy distribution via an intelligent control signal inaccordance with embodiments of the present invention. This techniqueinvolves a method of using the real time or recent charge estimates,determined from at least one of a current sensor (e.g., 380 of FIG. 3),learning algorithm (e.g., within control logic 160 and 135 of FIG. 1),or an API, to access network based vehicle data, for example via network150 (of FIG. 1), and use such data to allocate power to the electricvehicle or vehicles.

The technique can begin at 605 with a determination of whether there areuser requests. If there are no user requests at 605, then no smartresource allocation needs to be made, and therefore the flow proceeds to615. Otherwise, if there are user requests at 605, then the flow canproceed to 610 where a determination can be made whether there are toomany users. The determination of whether there are too many users can bea determination of whether the charging or power demand associated withthe number of electric vehicles present exceeds the total installedpower or amps, taking into account any buffer. If YES, meaning that thecharging need exceeds what is available (e.g., the total requested ampsexceeds the total installed amps minus the buffer), then the flow canproceed to 620, where charge cycle real-time demand estimates can bedetermined. Otherwise, if NO, meaning that the charging need does notexceed what is available (e.g., the total requested amps is less thanthe total installed amps minus the buffer), then the flow can proceed to615 where no smart allocation of resources is needed.

At 625, the estimates can be used to allocate minimum estimated need toeach vehicle. At 630, power is allocated to the vehicles according tothe minimum estimated need to each vehicle. At 635, a determination canbe made whether power should be reallocated. If YES, the flow can returnto 605 for further determinations and further charging. It will beunderstood that the steps and elements of FIG. 6 need not occur in theorder shown, but rather, can occur in a different order or withintervening steps, or without some of the steps.

FIG. 7 illustrates a graph demonstrating the regulation of the vehiclecharging in accordance with embodiments of the present invention. Thegraph shows a run check cycle 700. The current reading 720 from thecurrent sensor (e.g., 380 of FIG. 3) can be used to regulate the vehiclecharging to a lower maximum without inhibiting an electric vehicle'scharge time or charging cycle. As shown in this example, there are twopilot controlled maximum charge levels 740, which represent twodifferent maximum charge levels 740 for two different times. In otherwords, as the charging of the electric vehicle progresses along thecurrent reading curve 720, the pilot signal controlled max charge level740 can be progressively lowered. In some embodiments, the pilot signalcontrolled max charge level 740 can be incrementally lowered as thecharging of the electric vehicle progresses. Alternatively, the pilotsignal controlled max charge level 740 can be continuously lowered asthe charging of the electric vehicle progresses. The power managers(e.g., 305 of FIG. 3) can collectively or individually control the pilotsignal controlled max charge level over time for each electric vehiclebeing charged.

FIG. 8 illustrates a block diagram of a multi-circuitmulti-parking-level electric vehicle charging system 800, including alocal vehicle charging mesh network 880 in accordance with embodimentsof the present invention. It is quite common in large apartmentcomplexes or city-based parking garages to have multiple parking levels.The parking levels are often separated by thick concrete floors andwalls, which can impede the transmission of wireless signals. Powermanagers (PMs) can be located on each parking level. Each PM can beincorporated into Electric Vehicle Supply Equipment (EVSE) or otherwisebe separate from or connected to an EVSE. Each PM can be separate fromand connected to an EVSE.

For example, PM 820 can be located on level 1, PM 815 and PM 810 can belocated on level 2, and PM 805 can be located on level N. It will beunderstood that any suitable number of PMs and parking levels can bepart of the electric vehicle charging mesh network 800. A localshort-range wireless electric vehicle charging mesh network 880 caninterconnect the PM 805, the PM 810, the PM 815, and the PM 820. Inother words, the local vehicle charging mesh network 880 caninterconnect the short-range wireless transceivers of the PM 805, the PM810, the PM 815, and the PM 820. Each of the PMs can at least one ofreceive, process, retransmit, or store data packets that are transmittedon the local vehicle charging mesh network 880 by each of the PMs. Inother words, each of the PMs can at least one of receive, process,retransmit, or store all data packets that are transmitted on the localvehicle mesh network 880. It will be understood that any suitable numberof parking levels and PM units can be included in the local short-rangewireless electric vehicle charging mesh network 880. The effective reachof each short-range transceiver (e.g., 840, 845, 850, and 855) of eachPM is lengthened, since connection to one node is sufficient to accessthe entire network. In other words, each short-range wirelesstransceiver (e.g., 840, 845, 850, and 855) within the local electricvehicle charging mesh network 880 “sees” all packets that aretransmitted among the various nodes within the network. Thus, the reachof each node (i.e., PM) is expanded.

This technique is particularly useful in the multi-level garageapplication because as mentioned above, the natural conditions of theseenvironments prohibit the typical range of wireless signals. One of thePMs, e.g. PM 805, can include a long-range transceiver such as acellular transceiver 835 to connect the short-range wireless electricvehicle charging mesh network 880 to the Internet via a cellularconnection 885. The cellular connection 885 can connect the cellulartransceiver 835 to a cellular or radio tower 860, which can be connectedto the Internet. The PM 805 can be situated in a spot that is suitablefor cellular reception, which can be, for example, the top level of amulti-level parking structure. The long-range transceiver 835 of the PM805 can connect the local vehicle charging mesh network 880 to theremote server 145 via the cellular connection 885. The remote server 145can provide analytical information about the local vehicle charging meshnetwork 880, the electric vehicles associated with the vehicle chargingmesh network 880, the power managers associated with the vehiclecharging mesh network 880, the information stored thereon, and so forth.

Parking spaces are constructed of dense materials such as cement andsteel, which impede conventional wireless networking solutions. This inturn diminishes the coordination and communication of differentcomponents of a conventional charging system or network. The long-rangetransceiver 835 can be located on a level of a parking structure that ishigher in elevation than other levels, or at the highest level, so thata reliable connection can be made to the cellular tower 860 via thecellular connection 885. The long-range transceiver 835 can thus connectthe local vehicle charging mesh network 880 to the remote server 145 viathe cellular connection 885. Referring to FIG. 8, the level N of theparking structure can be higher in elevation than the level 2 of theparking structure, and the level 2 of the parking structure can behigher in elevation than the level 1 of the parking structure.

Each PM (e.g., 805, 810, 815, and 820) can be associated with a givencircuit (e.g., 825 and 830). For example, as shown in FIG. 8, PM 810, PM815, and PM 820 can be associated with circuit 830, whereas PM 805 canbe associated with circuit 825. It will be understood that any suitablenumber of PMs can be associated with a given circuit. It will beunderstood that any suitable number of circuits can be associated with aparticular location. In some embodiments, the association can also spanmultiple parking levels (e.g., PMs located on levels 1 and 2 areassociated with circuit 830). Each PM can include control logic (e.g.,135 of FIG. 1) that makes it aware of which circuit it is associatedwith. The PM can be assigned a circuit or breaker identifier (ID) (e.g.,890) using a dip switch, a configurable setting such as a memoryregister, or the like. The local electric vehicle charging mesh network880 can be collectively aware of which PMs are associated with whichcircuits, and can allocate electric vehicle charging resources and powerlevels accordingly.

The local electric vehicle charging mesh network 880 can record and logsystem change events 898 such as current levels of charging, electricvehicle disconnections, electric vehicle connections, chargingcompleting, charging starting, charging levels, and the like. The systemchange events 898 can be stored on each PM, such as in storage unit 895of PM 810. The change events can include at least one of a current levelof charging event for a particular electric vehicle, an electric vehicledisconnection event from a particular PM from among the various PMs, oran electric vehicle connection event to a particular PM from among thevarious PMs. The change events can include a charge completing event fora particular electric vehicle and a charge starting event for theparticular electric vehicle.

The system change events 898 can be associated with time and stored as ahistory of events, which can be used to predict future events based on aset of heuristics. In some embodiments, the history of events for eachPM can be stored on each PM. In some embodiments, the history of eventsfor a particular PM can be stored only for the particular PM. The localelectric vehicle charging mesh network 880 can include a learning logicsection (e.g., 888) to learn from the history and predict futurebehavior and future needs of EVs. Each PM can include the learning logicsection (e.g., 888) to learn the history for that particular PM. Each PMcan include the learning logic section (e.g., 888) to learn the historyfor each of the PMs. Each PM can include the learning logic section(e.g., 888) to learn the history for the associated EVs. The learninglogic section 888 can formulate or refine heuristics 892. The PMs cantrack individual electric vehicles using a vehicle identifier 876, suchas at least one of a radio frequency ID (RFID) tag or a near-fieldcommunication (NFC) tag. The PMs can track the individual electricvehicles via the local electric vehicle charging mesh network 880. EachPM can access the stored history (e.g., stored on 895) and apply theheuristics 892 to predict how much charge a particular electric vehiclewill need at a particular time of day. Each PM can access the storedhistory (e.g., stored on 895) and apply the heuristics 892 to predicthow much charge a particular electric vehicle will need based on aparticular day. Charging can be prioritized based on at least one of auser's usual arrival time, connection time, charging time, disconnectiontime, or time of leaving. Because all of the PMs have access to all ofthe data packets, change events, and stored history of the local vehiclecharging mesh network 880, a particular electric vehicle can plug intoany of the PMs, and the prediction techniques can still be used toimprove the charging experience of the particular electric vehicle. ThePMs can allocate among themselves the available power for each electricvehicle based at least on immediate demand or the heuristics 892. ThePMs can provide a predictive readout of total charge time (e.g., currentdraw, charge length, position in queue, and the like) based at least onthe heuristics 892. The PMs can reconfigure the level of charge providedto each electric vehicle based on the total power available for a givencircuit. Because reconfiguring is an expensive operation in terms oftime and electric vehicle charging protocol limitations, the amount ofreconfiguring needed can be reduced by relying on the predictiveheuristics 892.

For example, if a particular electric vehicle is known to only tricklecharge at a particular time of day, then a trickle charge can be appliedfrom the start, i.e., at the time the electric vehicle plugs in andrequests a charge. By way of another example, a PM might determine thatat 2 PM on weekdays a particular electric vehicle will need a fullcharge. By way of yet another example, just because an electric vehicleis first to plug into a given PM associated with a given circuit, thatdoes not necessarily mean that that electric vehicle receives a full ormaximum charging power level, but rather, based on the heuristics 892,that electric vehicle may receive from the start a reduced level ofcharging power.

The PMs can determine where each electric vehicle is in the chargingcycle and adjust at least one of current or power levels up or down,using for example, the pilot signal described above. The charging cycledata can be represented and stored on at least one of the storage unit895 or the remote server (e.g., 145 of FIG. 1) as graphs, logs, changeevent lists, or the like. The PMs can determine whether the current dayis a weekday or a weekend day and adjust accordingly. The heuristics 892can also be used to determine safety buffers in the charging, and cantake into account history and immediate needs. For example, theheuristics 892 can include information regarding the maximum installedpower for a given location and the built-in buffer for adhering togovernment code. The PMs can provide predictive information to electricvehicle owners, such as estimations of time of charge completion,charging rate, or the like. Such information including the heuristics,the graphs, the logs, the change event list, the predictive information,or the like, can be stored on in the database 155 of the remote server145. Electric vehicle owners can remotely access such information via amobile device such as a smart phone or tablet (e.g., 894).

A particular PM (e.g., 805, 810, 815, and 820) can determine when aparticular electric vehicle requests and receives a charge. Theparticular PM can determine an amount of charge that is needed tocomplete the charge. The PM can analyze past charge currents or events.The PM can match the pilot signal (e.g., 340 of FIG. 3) to draw acurrent that is equal to a predictive current draw for a particularelectric vehicle based on the heuristics 892. In the event that anelectric vehicle requests a “normal” maximum charging level, and the“normal” maximum charging level is unavailable due to circuitconstraints, then the PM can use the heuristics 892 to determine acharging level that is less than the “normal” maximum charging level.The “less-than-normal” charging level can be a charge level that is lessthan the “normal” maximum charging level. For example, based on theheuristics 892, the PM can cause the less-than-normal charging level tobe used to charge the particular electric vehicle.

FIG. 9 illustrates a diagram of a multi-circuit multi-parking-levelapartment complex 905 including distributed power managers (e.g., PMs950 and 955) for controlling the distribution of power to electricvehicles (e.g., electric vehicle 945) and to other apartment appliances(e.g., lights 960) in accordance with embodiments of the presentinvention. It will be understood that while the term apartment complexis used with reference to FIG. 9 for the sake of illustrating anexemplary embodiment, other parking structures not necessarilyassociated with apartment complexes are equally applicable and suitablefor incorporating the various embodiments of the invention as describedherein. The apartment complex 905 can have an electrical mains 962 lineassociated therewith. The electrical mains 962 can be connected to atransformer 910. The transformer 910 can step down the voltage receivedover the mains 962. The transformer 910 can be connected to anelectrical disconnect unit 915. The electrical disconnect unit 915 canbe connected to a circuit panel board 920. The electrical disconnect 915can disconnect or connect the transformer 910 from or to the circuitpanel board 920. The circuit panel board 920 can include varioussub-panels (e.g., 925 and 930). For example, the circuit panel board 920can include a main panel 925 used for providing power to apartmentappliances such as lights 960, plug outlets 972, or the like.Alternatively or in addition, the circuit panel board 920 can includeone or more sub-panels 930 used for providing power charging to theelectric vehicles 945.

The one or more sub-panels 930 can include multiple circuits (e.g.,circuit 1 and circuit 2), with associated breakers (e.g., breaker 935and breaker 940) for each circuit. The circuit 1 can be associated witha sub-set of PMs, such as those PMs located on level N (i.e., LN) of theparking garage. The circuit 2 can be associated with a different sub-setof PMs, such as at least one of a portion of the PMs on level 1 (i.e.,L1) or a portion of the PMs on level 2 (i.e., L2) of a parking garage ofthe complex 905. A local electric vehicle charging mesh network 880, asdescribed above with reference to FIG. 8, can interconnect the PMsthroughout the apartment complex 905 and the associated parking garage982 including those PMs associated with charging the electric vehicles945, and those PMs (e.g., 955) associated with powering other appliancessuch as the lights 960 or via the plug outlet 972.

Measurement points can be located along the chain of power supplycomponents. For example, a measurement point can be located at 965associated with the mains 962. A measurement point can be located at 970between the transformer 910 and the disconnect 915. A measurement pointcan be located at 975 between the disconnect 915 and the circuit panelboard 920, and so forth. The local electric vehicle charging meshnetwork 880 (of FIG. 8) can monitor the measurement points along thischain, and adjust the distribution of power throughout the apartmentcomplex 905 responsive to the measurements. For example, the localelectric vehicle charging mesh network 880 can monitor the aggregateamount of power available relative to the aggregate amount of powercurrently being consumed. The local electric vehicle charging meshnetwork 880 can automatically reconfigure an amount of power being usedby each circuit within the circuit panel board 920. In the case wherethere are more installed circuits than what the transformer 910 cansafely support in a simultaneous fashion, the local electric vehiclecharging mesh network 880 can cause power to disabled to certaincircuits and power to be enabled to other circuits during different timeperiods. The local electric vehicle charging mesh network 880 can rankand prioritize which circuits get power or the level of power availableto each circuit. At the individual PM level, each PM within the localvehicle charging mesh network 880 can determine how much power (e.g.,based on a current level controlled by a pilot signal such as 340 ofFIG. 3) a particular electric vehicle is allowed to draw in view of thepresent state of the local electric vehicle mesh network 880. In otherwords, each PM can be self-aware of the overall power usage across thenetwork of PMs in view of the power limitations of each circuit, and inview of the power limitations of the transformer 910, and makeautonomous charging decisions based at least on such information. ThePMs can also rely on predictive heuristics (e.g., 892 of FIG. 8) tobalance the distribution of the available power among the electricvehicles and other apartment appliances.

It will be understood that even though the electric vehicle chargingnetwork is preferably a local electric vehicle charging mesh network 880as described herein, the network can also be configured in aserver/client arrangement, or a master/slave arrangement.

The following discussion is intended to provide a brief, generaldescription of a suitable machine or machines in which certain aspectsof the invention can be implemented. Typically, the machine or machinesinclude a system bus to which is attached processors, memory, e.g.,random access memory (RAM), read-only memory (ROM), or other statepreserving medium, storage devices and units, a video interface, andinput/output interface ports. The machine or machines can be controlled,at least in part, by input from conventional input devices, such askeyboards, mice, etc., as well as by directives received from anothermachine, interaction with a virtual reality (VR) environment, biometricfeedback, or other input signal. As used herein, the term “machine” isintended to broadly encompass a single machine, a virtual machine, or asystem of communicatively coupled machines, virtual machines, or devicesoperating together. Exemplary machines include computing devices such aspersonal computers, workstations, servers, portable computers, handhelddevices, telephones, tablets, etc., as well as transportation devices,such as private or public transportation, e.g., automobiles, trains,cabs, etc.

The machine or machines can include embedded controllers, such asprogrammable or non-programmable logic devices or arrays, ApplicationSpecific Integrated Circuits (ASICs), embedded computers, smart cards,and the like. The machine or machines can utilize one or moreconnections to one or more remote machines, such as through a networkinterface, modem, or other communicative coupling. Machines can beinterconnected by way of a physical and/or logical network, such as anintranet, the Internet, local area networks, wide area networks, etc.One skilled in the art will appreciate that network communication canutilize various wired and/or wireless short range or long range carriersand protocols, including radio frequency (RF), satellite, microwave,Institute of Electrical and Electronics Engineers (IEEE) 545.11,Bluetooth®, optical, infrared, cable, laser, etc.

Embodiments of the invention can be described by reference to or inconjunction with associated data including functions, procedures, datastructures, application programs, etc. which when accessed by a machineresults in the machine performing tasks or defining abstract data typesor low-level hardware contexts. Associated data can be stored in, forexample, the volatile and/or non-volatile memory, e.g., RAM, ROM, etc.,or in other storage devices and their associated storage media,including hard-drives, floppy-disks, optical storage, tapes, flashmemory, memory sticks, digital video disks, biological storage, etc.Associated data can be delivered over transmission environments,including the physical and/or logical network, in the form of packets,serial data, parallel data, propagated signals, etc., and can be used ina compressed or encrypted format. Associated data can be used in adistributed environment, and stored locally and/or remotely for machineaccess.

Having described and illustrated the principles of the invention withreference to illustrated embodiments, it will be recognized that theillustrated embodiments can be modified in arrangement and detailwithout departing from such principles, and can be combined in anydesired manner. And although the foregoing discussion has focused onparticular embodiments, other configurations are contemplated. Inparticular, even though expressions such as “according to an embodimentof the invention” or the like are used herein, these phrases are meantto generally reference embodiment possibilities, and are not intended tolimit the invention to particular embodiment configurations. As usedherein, these terms can reference the same or different embodiments thatare combinable into other embodiments.

Embodiments of the invention may include a non-transitorymachine-readable medium comprising instructions executable by one ormore processors, the instructions comprising instructions to perform theelements of the inventive concepts as described herein.

Consequently, in view of the wide variety of permutations to theembodiments described herein, this detailed description and accompanyingmaterial is intended to be illustrative only, and should not be taken aslimiting the scope of the invention. What is claimed as the invention,therefore, is all such modifications as may come within the scope andspirit of the following claims and equivalents thereto.

The invention claimed is:
 1. An electric vehicle charging system,comprising: a remote server; a plurality of power managers including afirst power manager having a learning logic section and a storage unit,and a second power manager having a learning logic section and a storageunit, each of the first and second power managers including ashort-range wireless transceiver, and each of the first and second powermanagers including a connector configured to charge a first electricvehicle and a second electric vehicle, respectively; one or more thirdpower managers each including a learning logic section and a storageunit, a short-range wireless transceiver, a long-range transceiver, anda connector configured to charge a third electric vehicle; and a localelectric vehicle charging mesh network configured to interconnect theshort-range wireless transceivers of the first power manager, the secondpower manager, and the one or more third power managers to each other,wherein each of the first power manager, the second power manager, andthe one or more third power managers is configured to receive datapackets that are transmitted by each of the other first power manager,the second power manager, and the one or more third power managers onthe local electric vehicle charging mesh network; wherein the long-rangetransceiver of the one or more third power managers is configured toconnect the local electric vehicle charging mesh network to the remoteserver; wherein the storage unit of the first power manager isconfigured to store change events associated with the first powermanager, the second power manager, and the one or more third powermanagers; wherein the change events include at least one of a currentlevel of charging event for a particular electric vehicle, adisconnection event for the particular electric vehicle, a connectionevent for the particular electric vehicle, a charge starting event forthe particular electric vehicle, or a charge completing event for theparticular electric vehicle; wherein the storage unit of the secondpower manager is configured to store the change events associated withthe first power manager, the second power manager, and the one or morethird power managers; wherein the storage unit of the one or more thirdpower managers is configured to store the change events associated withthe first power manager, the second power manager, and the one or morethird power managers; wherein the learning logic section of the firstpower manager is configured to learn a history associated with the firstpower manager, a history associated with the second power manager, and ahistory associated with the one or more third power managers based onthe change events stored in the storage unit of the first power manager,and to predict future vehicle charging events and a predictive currentdraw for each of the first, second, and third vehicles based on thelearned histories; and wherein the first power manager, the second powermanager, and the one or more third power managers each include controllogic having an assigned circuit identifier that is associated with oneof a plurality of circuits each having an available amount of powerassociated therewith such that the local electric vehicle charging meshnetwork is collectively aware of which power manager is associated withwhich circuit from among the plurality of circuits and the availableamount of power of each of the plurality of circuits, wherein the localelectric vehicle charging mesh network is configured to intelligently,efficiently, and unevenly allocate different power levels to the first,second, and third electric vehicles based on the predictive current drawand the collective awareness of the available amount of power of each ofthe plurality of circuits.
 2. The electric vehicle charging system ofclaim 1, wherein the first power manager, the second power manager, andthe one or more third power managers are each configured to receive,process, and retransmit the data packets that are transmitted on thelocal electric vehicle charging mesh network by each of the first powermanager, the second power manager, and the one or more third powermanagers, wherein the learning logic section of the second power manageris configured to learn the history associated with the first powermanager, the history associated with the second power manager, and thehistory associated with the one or more third power managers based onthe change events stored in the storage unit of the second powermanager, and to predict the future charging events based on the learnedhistories; and wherein the learning logic section of the one or morethird power managers is configured to learn the history associated withthe first power manager, the history associated with the second powermanager, and the history associated with the one or more third powermanagers based on the change events stored in the storage unit of theone or more third power managers, and to predict the future chargingevents based on the learned histories.
 3. The electric vehicle chargingsystem of claim 1, wherein: the local electric vehicle charging meshnetwork is configured to transmit the change events among each of thefirst power manager, the second power manager, and the one or more thirdpower managers.
 4. The electric vehicle charging system of claim 3,wherein the change events include at least one of a current level ofcharging for a particular electric vehicle, an electric vehicledisconnection event from a particular power manager from among the firstpower manager, the second power manager, and the one or more third powermanagers, or an electric vehicle connection event to a particular powermanager from among the first power manager, the second power manager,and the one or more third power managers.
 5. The electric vehiclecharging system of claim 3, wherein the change events include at leastone of a charge completing event for a particular electric vehicle or acharge starting event for the particular electric vehicle.
 6. Theelectric vehicle charging system of claim 3, wherein the learning logicsection of the first power manager, the learning logic section of thesecond power manager, and the learning logic section of the one or morethird power managers are each configured to predict, based at least onthe stored change events, how much charge a particular electric vehiclewill need at a particular time of day.
 7. The electric vehicle chargingsystem of claim 3, wherein the learning logic section of the first powermanager, the learning logic section of the second power manager, and thelearning logic section of the one or more third power managers are eachconfigured to predict, based at least on the stored change events, howmuch charge a particular electric vehicle will need on a particular day.8. The electric vehicle charging system of claim 3, wherein the learninglogic section of the first power manager, the learning logic section ofthe second power manager, and the learning logic section of the one ormore third power managers are each configured to dynamically determine apercentage allocation of power for each of the first electric vehicle,the second electric vehicle, and the third electric vehicle relative toa maximum amount of installed power for a given location and a built-inbuffer for adhering to government code.
 9. The electric vehicle chargingsystem of claim 3, wherein: the learning logic section of the firstpower manager, the learning logic section of the second power manager,and the learning logic section of the one or more third power managersare each configured to determine a normal charging level for aparticular electric vehicle and a less-than-normal charging level forthe particular electric vehicle; and the learning logic section of thefirst power manager, the learning logic section of the second powermanager, and the learning logic section of the one or more third powermanagers are each configured to determine whether the particularelectric vehicle should receive the normal charging level or theless-than-normal charging level.
 10. The electric vehicle chargingsystem of claim 3, wherein the learning logic section of the first powermanager, the learning logic section of the second power manager, and thelearning logic section of the one or more third power managers are eachconfigured to prioritize charging of a particular electric vehicle basedon at least one of a usual arrival time, a connection time, a chargingtime, or a disconnection time.
 11. The electric vehicle charging systemof claim 10, wherein the learning logic section of the first powermanager, the learning logic section of the second power manager, and thelearning logic section of the one or more third power managers are eachconfigured to learn from the history that is based on the change events,and to predict future charging behavior or needs of the first, second,and third electric vehicles, respectively.
 12. The electric vehiclecharging system of claim 10, further comprising an electrical circuit,wherein the learning logic section of the first power manager, thelearning logic section of the second power manager, and the learninglogic section of the one or more third power managers are configured toreconfigure a level of charge provided to the first electric vehicle,the second electric vehicle, and the third electric vehicle,respectively, based at least on a total power available for theelectrical circuit.
 13. The electric vehicle charging system of claim10, wherein the learning logic section of at least one of the firstpower manager, the second power manager, or the one or more third powermanagers is configured to provide a reduced level of charging power toan electric vehicle from among the first, second, and third electricvehicles that is first to plug into a corresponding power manager. 14.The electric vehicle charging system of claim 10, wherein: the changeevents include the usual arrival time, the connection time, the chargingtime, and the disconnection time; and the first power manager, thesecond power manager, and the one or more third power managers areconfigured to prioritize charging of the plurality of electric vehiclesbased at least on all of the usual arrival time, the connection time,the charging time, and the disconnection time.
 15. The electric vehiclecharging system of claim 10, wherein: the learning logic section of atleast one of the first power manager, the second power manager, or theone or more third power managers is configured to determine that aparticular electric vehicle is known to only trickle charge at aparticular time of day; and at least one of the first power manager, thesecond power manager, and the one or more third power managers isconfigured to cause the particular electric vehicle to be tricklecharged from a time that the particular electric vehicle plugs in andrequests a charge from a power manager from among the first powermanager, the second power manager, and the one or more third powermanagers.
 16. The electric vehicle charging system of claim 15, whereina rate of the trickle charge is less than a rate of a normal charge. 17.The electric vehicle charging system of claim 10, wherein: the learninglogic section of at least one of the first power manager, the secondpower manager, or the one or more third power managers is configured todetermine the predictive current draw for a particular electric vehicle;and at least one of the first power manager, the second power manager,or the one or more third power managers is configured to match a controlsignal to draw a current that is equal to the predictive current drawfor the particular electric vehicle.
 18. The electric vehicle chargingsystem of claim 17, wherein: the control signal is an electric vehiclepilot signal; the long-range transceiver is a long-range cellulartransceiver; and the long-range transceiver of the one or more thirdpower managers is configured to connect the local electric vehiclecharging mesh network to the remote server via a cellular connection.19. The electric vehicle charging system of claim 1, further comprisinga plurality of electric vehicles including the first, second, and thirdelectric vehicles, wherein the first power manager, the second powermanager, and the one or more third power managers are configured totrack via the local electric vehicle mesh network a plurality ofelectric vehicle identifiers associated with the corresponding pluralityof electric vehicles.